How To Understand AI and Data for Beginners
Artificial intelligence has an almost infinite ability to process data in ways that create tremendous value for marketers. But, how do brands go from reading about AI to actually using it?
It starts with asking the right questions about your data. After all, data powers all artificial intelligence solutions. What AI can and can’t do for your brand depends on the type and amount of data you have.
Need direction to get started? AI Academy for Marketers is an online educational platform that helps you understand, pilot, and scale AI. We created this platform to help marketers gain access to affordable, AI-focused education that advances learning and aids in AI implementation.
Artificial Intelligence and Data for Beginners is one of the Academy’s certification courses for members. In the course, Cal Al-Dhubaib (@caldhubaib) of Pandata uncovers everything you need to know about the relationship between AI and data.
By understanding the impact of AI and data combined, you’ll have an advantage that sets you apart from competitors who aren’t seeking out information like this (props to you). In this post, we’ll provide key insights from Cal’s course. But before we dive into data, let’s decode the buzzword “AI” into a concept that actually makes sense.
AI Foundations & Value Propositions
AI is software that automates tasks that typically require human intelligence. AI systems recognize and react to complex patterns and continue to improve over time. Organizations are tapping into AI to create value in 3 primary ways: reducing costs, increasing efficiency, and growing revenue.
Businesses recognize that AI is critical but most struggle to scale their initiatives. What’s holding them back?
AI Adoption in the Enterprise 2020 O’Reilly reports that:
- 23% of employees don’t recognize the need for AI
- 20% have difficulties identifying appropriate business cases
- 17% say it’s lack of skilled people/difficulty hiring
- 16% say there is a lack of data or data quality issues
So, if companies are having trouble understanding the potential of AI, why is it the answer?
The main value propositions of AI include:
1. Automation: In our day-to-day, there are many repetitive tasks that we have to complete that keep us from spending more time on specialized tasks and opportunities. Use AI to train and recognize key themes in customer feedback instead of a manual review.
2. Reducing error: Humans make mistakes, it’s what makes us human. AI presents an incredible opportunity to reduce human error in our work.
3. Reducing risk: In some cases, certain tasks may be dangerous or prone to bias.
4. Increase availability: Even the most productive humans can’t and shouldn’t work 24 hours a day.
5. Increase speed: It would take a human several hours to review even an hour’s worth of recorded customer service calls and summarized key themes.
Some examples of AI capabilities include speech recognition, recommended actions, language generation, expert systems, intent recognition, and image recognition. In Cal’s course, he digs into these value propositions, and the corresponding opportunities they present.
Your Crash Course in Data
Big data and AI seem to go together synonymously, but why does it require so much data and what data do we need to succeed?
When we say data, we are really talking about two key concepts: data for training and inputs to the AI system in production.
Data for training requires larger data sets that are diverse and representative. Data for training is needed to design these AI solutions. Inputs to the AI system in production are handled on a case by case basis and can be very small. Inputs to the AI system in production are needed to run AI solutions.
The two types of data when it comes to machine learning are structured and unstructured.
Structured data fits neatly into a table or set of tables. The types of structured data include numerical (numbers), categorical (descriptions) and dates (appears as text but ordered as numbers).
Unstructured data consists of text (anything that doesn’t fit neatly into a category, such as names or comments), image (pixels are becoming important in AI applications with facial recognition to object detection) and audio (tone of voice, speed, intonation). The challenge with unstructured data is that it doesn’t fit into neat tables such as structured data.
Metadata is data that’s used to describe other forms of data. Metadata is equally as important as the data itself. You’ll need metadata for a variety of reasons like knowing the date it was created, the location, owner, copyright, and permission to use for machine learning purposes.
The AI Project Journey
If you’re ready to begin your first project with AI, Cal notes to be aware of the three phases. During the project journey, we focus on planning, designing, and scaling.
Planning an AI solution starts with assumptions around a specific task. First, we define the task and understand the goals of the stakeholders. Next, we define the value proposition by quantifying the impact of the solution. After, we confirm the availability of the data. Data is the fuel that AI depends on to learn and function. It’s okay if you’re unsure of where data lives and where to access it. If you have unknowns at this phase, it will help frame conversations with the team and vendors.
The design phase is focused on taking data and the problem statement to create the technical solution. The designing phase works in three stages: discovery, experiment and learning, and proof of concept.
Discovery focuses on deep diving into how the task is completed today and uncovering the tribal knowledge (assumptions we don’t explicitly think about). After, we try to understand the availability, quality, and suitability of data to begin our research with methodology tools.
Experiment and learning is associated with developing statistical models, trial and error, and building models that validate or invalidate assumptions.
Proof of concept is the prototype of the final solution. The goal is to get feedback from the stakeholders to determine if the solution you built is valuable.
Scaling means taking the solution and getting it in the hands of the users. In the scaling stage, we deploy our solutions to the real world, build resilience when things go wrong (and that’s expected), and educate and empower users after all deployments.
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